The Stanford Center for Systems Biology of Cancer (CCSB) aims to discover molecular mechanisms underiying cancer progression by studying cancer as a complex biological system that is driven, in part, by impaired differentiation. Increasing evidence indicates that many cancers, like normal tissue, are composed of a hierarchy of cells at different stages of differentiation, and that the disease is maintained hy a self-renewing subpopulation. Our overarching goal is to provide a better understanding of the self-renewing properties of cancer that will enable us to identify molecular therapeutic targets and strategies to eradicate this disease, or to maintain it in a nonlethal state. Our biological projects are integrated with novel computational techniques, designed to dissect processes and causal factors underlying impaired differentiation as a driver of cancer progression in several hematologic malignancies. This approach will enable us to ascertain differences between these malignancies, and commonalities which may generalize to other cancers. In order to identify mechanistic underpinnings of cancer progression, a network-based and multiscale viewpoint is mandatory. Increasingly, diseases such as cancer are recognized as resulting from disruption in the coordinated performance of a complex biological system. This systems biology viewpoint necessitates the incorporation of high throughput, high dimensional data, and development of computational methods specifically geared to its analysis. There are three essential and interiocking requirements for a comprehensive systems analysis of cancer. First, powerful methods are required to infer molecular regulatory networks that drive phenotypic processes such as differentiation. Second, computational approaches are needed that can identify and isolate underlying patterns of progression in cancer, which can then be related to underlying regulatory networks. Third, executable models are desirable so that it is possible to pose hypothetical what if questions to predict how, for example, a targeted intervention might affect the subsequent course of disease. These computational approaches will be applied to the study of differentiation in AML, Follicular Lymphoma and T-ALL. In AML, we will identify regulatory networks driving leukemic stem cells. In Follicular Lymphoma, we will analyze the relationship between BCR-sensitive and BCR-insensitive subpopulations. In T-ALL, we will study the self-renewing properties of MYC in a transgenic mouse model. Our integrative approach will enable us to ascertain differences between these hematologic malignancies, and commonalities which may generalize to other cancers. In addition, the Stanford CCSB's efforts in education and outreach aim to provide the next generation of cancer researchers with a solid foundation in ntegrative experimental and computational methods of scientific research.